Amplification of temperature extremes in Arabian Peninsula under warmer worlds

The Paris Agreement and the Special Report on Global Warming of 1.5 °C from the Intergovernmental Panel on Climate Change (IPCC) highlighted the potential risks of climate change across different global warming levels (GWLs). The increasing occurrence of extreme high-temperature events is linked to a warmer climate that is particularly prevalent in the Arabian Peninsula (AP). This study investigates future changes in temperatures and related extremes over AP, under four GWLs, such as 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C, with three different Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study uses high-resolution datasets of 27 models from the NASA Earth Exchange Global Daily Downscaled Projections of the Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6). The results showed that the NEX-GDDP-CMIP6 individual models and their multi-model means reasonably captured the extreme temperature events. The summer maximum and winter minimum temperatures are projected to increase by 0.11–0.67 °C and 0.09–0.70 °C per decade under the selected SSPs. Likewise, the projected temperature extremes exhibit significant warming with varying degrees across the GWLs under the selected SSPs. The warm temperature extremes are projected to increase, while the cold extremes are projected to decrease under all GWLs and the selected SSPs. Overall, the findings provide a comprehensive assessment of temperature changes over AP in response to global warming, which can be helpful in the development of climate adaptation and mitigation strategies.


Data and methodology
This study used the daily maximum temperature (T max ) and minimum temperature (T min ) outputs from 27 biascorrected models under the SSP1-2.6,SSP2-4.5, and SSP5-8.5 scenarios (Table 1), which are prepared under the NEX-GDDP data from CMIP6 global climate model outputs (https:// www.nccs.nasa.gov/ servi ces/ data-colle ctions/ land-based-produ cts/ nex-gddp-cmip6).The bias correction spatial disaggregation (BCSD) algorithm is used to produce a high-resolution (0.25° × 0.25°) bias-corrected and downscaled dataset.The BCSD method represents a trend-sustaining statistical downscaling technique and is widely used in multiple meteorological studies 19,31,32 .The datasets span from the historical period from 1951-2014 and future projections (2015-2100) for SSP1-2.6,SSP2-4.5, and SSP5-8.5 scenarios 20 .In addition, the ERA5 reanalysis temperature dataset was obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation atmospheric reanalysis with a spatial resolution of 0.25° × 0.25°3 3 .The ERA5 dataset is used as a reference to evaluate the performance of selected models in simulating the historical temperatures.The IPCC Sixth Assessment Report (AR6) recommended the period 1995-2014 as the reference period for calculating changes in climate extreme indices 1 .For calculating future projections, using this reference period with relatively stable climate conditions may provide more accurate projections in a global warming world.We used the multi-model mean (MMM) to project future changes in temperature extremes under different GWLs and SSPs.This approach helps to reduce the intermodal uncertainties and biases, originating from the individual models 23 .We selected eight extreme temperature indices, among which seven indices were from the recommended list of the Expert Team on Climate Change Detection and Indices (ETCCDI) by the World Meteorological Organization (WMO) (Table 2).Following Odnoletkova and Patzek's approach 7 , we have added one new extreme temperature index, namely hot days (HDs).The HDs index is defined as "the annual count of days when the daily T max reaches 37.8 °C (100°F)".Recent studies state that the absolute values-based indices, i.e., HDs (T max > 37.8 °C) and tropical nights (TR: T min > 20 °C) are relatively more representative of the AP's extreme climatic features and provide more meaningful information than the ones recommended by the ETCCDI 7,34 .Moreover, the significance of the projected spatial changes in temperatures and their extremes under the selected GWLs is determined using Student's t-test at a 99% confidence level.
The CMIP6 model runs were developed to support the IPCC AR6.These datasets are recommended for assessing climate change impacts on processes sensitive to finer-scale gradients and the effects of local topography on climate conditions.In this study, we have adopted the GWL periods recommended by the IPCC AR6.These GWLs are classified as follows: 1.5 °C (2023-2042, 2021-2040, 2018-2037), 2 °C (never, 2043-2062, 2032-2051), 3 °C (never, never, 2055-2074), and 4 °C (never, never, 2075-2094) under the SSP1-2.6,SSP2-4.5, and SSP5-8.5 scenarios 1 .Additionally, to confirm these GWL periods for individual models, the starting and ending years of the relevant warming thresholds from CMIP6 (raw) models are provided in Table S1 35 .The selection of SSP1-2.6,SSP2-4.5, and SSP5-8.5 aimed to cover a spectrum of potential future trajectories, encompassing low, medium, and high-emission scenarios, respectively.In this study, we excluded the SSP3-7.0scenario due to data insufficiency compared to the number of models available for other SSP scenarios.The IPCC Synthesis Report also mentioned that the GWLs are used to integrate the assessment of climate change and related impacts and Table 1.Description of the selected NEX-GDDP-CMIP6 models for this study 20 .

Model name
Institution/Country  www.nature.com/scientificreports/risks since patterns of changes for many variables at a given GWLs are common to all scenarios considered and independent of timing when that level is reached 36 .This research delves into the implications of 1.5 °C, 2 °C, 3 °C, and 4 °C of GWLs across the AP, as countries grapple with diverse challenges stemming from climate change-induced extremes.The IPCC AR6 recently outlined the methodologies employed to determine the year when specific GWLs were first attained 1 , with similar approaches discussed in studies [37][38][39] .In conducting time series analysis, they adhere to the standard categorization of models based on their SSPs (SSP1-2.6,SSP2-4.5, and SSP5-8.5).When examining climatological averages, they adopt the framework of GWLs at 1.5 °C, 2 °C, 3 °C, and 4 °C rather than focusing on RCPs or SSPs at specific time intervals 40,41 .These GWLs are defined as the periods during which the 20-year running global average temperature reaches the corresponding level of change compared to the pre-industrial era of 1850-1900 42 .The present work adopts a set of common GWLs across which climate projections can be integrated.Here, the temperature extremes were calculated for the future period at GWLs of 1.5 °C, 2 °C, 3 °C, and 4 °C.

Model evaluation
The annual cycle of T max , T min , and T mean over the AP is shown from 1995 to 2014, using the MMM of NEX-GDDP-CMIP6 and ERA5 (Fig. S1a-c).The NEX-GDDP-CMIP6 MMM and ERA5 datasets showed similar patterns of monthly temperatures during the year, with higher values during the summer months (June to September) and lower values in the winter months (December to February).The magnitude of T max , T min , and T mean are relatively less in the MMM than in the ERA5.The current study made use of the single realization of the selected climate models.Since we used the average of 27 models, to obtain a large MMM with their single realization, which also reduces the uncertainties in portraying the climate sensitivity, compared to each single model 43 .Furthermore, bringing all the selected climate models to uniform spatial resolution and integrating them into an MMM will certainly be helpful for the computational purpose and conventional evaluation of the models 44 .The annual mean values of T max , T min , and T mean from the ERA5 and MMM are 32.7 (31.5) °C, 18.8 (19.0) °C, and 26.1 (25.6) °C respectively.Figure 1a,b show the root mean square (RMSE) and the standard deviation (SD) of all the individual models along with their NEX-GDDP-CMIP6 MMM and ERA5 for all the temperature extreme indices from 1995 to 2014.This analysis helps to understand the interannual variability and biases in the individual simulations when compared with the MMM and the reference dataset of ERA5.The RMSE and SD of MMM and ERA5 agree with varied differences in the respective models.Interestingly, the RMSE is relatively high (> 30) for HDs in all models, including MMM, and is followed by TR.Unlike the RMSE, the SD has shown higher values in most of the models, such as CMCC-ESM2, EC-Earth3, EC-Earth3-Veg-LR, INM-CM 4-8, NESM3, and NorESM-MM than the ERA5 and MMM in the case of TR.More SD is observed in TR, followed by HDs in all models, which can be attributed to their fixed threshold criteria.In such cases, it is difficult to capture the spatial relativity, when calculating the spatial average over a large area with different climatologies.The T max of JJAS and T min of DJF have shown good agreement with the ERA5 in all the model simulations, including MMM in the case of RMSE.The T max of JJAS, T min of DJF, and TXx showed a similarity in the magnitude of SD in all the models and MMM with the ERA5.Furthermore, a lesser difference is observed in the values of RMSE and SD of MMM in comparison with the ERA5.

Future projections of temperature and extreme events
Figure 2 displays the temporal evolution of the projected changes in summer T max and winter T min under SSP1-2.6,SSP2-4.5, and SSP5-8.5 scenarios.An increasing trend is observed in both T max and T min .In particular, the warming is more pronounced from 2050 onward to the end of the twenty-first century in all the SSPs with varying degrees of warming.Relative to the reference period of 1995-2014, the high emission scenario (SSP5-8.5)has shown the maximum warming by yielding to + 6 °C in both the cases of T max and T min .The summer T max and winter T min are projected to increase by 0.67 °C and 0.70 °C per decade respectively under SSP5-8.5.The medium (SSP2-4.5)and low (SSP1-2.6)scenarios showed a warming of + 2.9 °C and + 1.5 °C relative to the reference period.Projected increases of 0.11 °C (T max ) and 0.09 °C (T min ), as well as 0.31 °C (T max ) and 0.28 °C (T min ) per decade, are anticipated under the SSP1-2.6 and SSP2-4.5 scenarios respectively.
The monthly mean values of the temperatures for all the decades starting from 1951-1960 till 2091-2100 under the historical, SSP1-2.6,SSP2-4.5, and SSP5-8.5 scenarios are illustrated in Fig. 3.It is conspicuous that the monthly temperatures gradually increased from the decade of 1950s.This warming is more prominent in the SSP5-8.5 scenario than in the other two scenarios.It is clear from all the months, irrespective of the wet/ dry season, that the warming is notable, up to 6 °C in most of the months and beyond 6 °C in June, September, October, and November months during the SSP5-8.5 scenario.A two-shift pattern has been observed in recording the elevated temperatures over the AP.The temperatures experienced a rise of 0.5 °C till the 1990s; thereafter the increase escalated to 1.6 °C, 3.0 °C and 6.0 °C under SSP1-2.6,2-4.5, and 5-8.5 scenarios, respectively, toward the end of the century.
An enormous amount of data indicates global warming has significantly increased the frequency and intensity of several climate extremes in the past few decades 8,45 .The annual temporal anomalies of temperature extremes over the AP region from MMM and ERA5 datasets for historical and future periods (1951-2100), relative to the reference period (1995-2014), are shown in Fig. 4. The results indicate that the daily temperature extremes, such as TX90p, TN90p, HDs, and TR, have shown an increasing trend in all SSP scenarios; however, SSP5-8.5 has a relatively higher magnitude of 77%, 82%, 90 days, and 106 days respectively, when compared with the other two scenarios by the year 2100.On the other hand, the daily cold temperature extremes, including the TX10p and TN10p, have shown a decreasing trend by the end of the twenty-first century in all SSP scenarios.The occurrence of TX10p and TN10p was between 20 to 30% during the 1950s, relative to the reference period; however, their magnitude has declined to − 10% by the end of the century.Figure 4 shows that the variability of ERA5 is higher than MMM in the temporal series of all temperature extremes during the historical period.The projected relative changes in summer T max , and winter T min over the AP for the SSP1-2.6,SSP2-4.5, and SSP5-8.5 under the 1.5 °C-4 °C GWLs are shown in Fig. 5.The summer T max and winter T min are increasing across the AP, with T max warming greater towards the north and central parts of AP (Fig. 5a-l).These changes are more prominent in the northern AP (beyond 2.0 °C warming) than in the southern AP (less than 2.0 °C warming).However, Oman did not show any significant change, though a warming tendency was observed.This phenomenon could be seen in Oman and some parts of the United Arab Emirates (UAE), which have mountains as well as long coastal lines, this may be one of the reasons to offset the extreme temperatures.Under the scenario of 3 °C warming, substantial changes in SSP5-8.5 are projected with a rise in temperature beyond 3.5 °C in the northern parts of AP and less than 2.5 °C in the southern parts, particularly in Yemen and Oman.The dominance of northern parts of AP can be seen in simulating the summer T max of more than 5.5 °C under SSP5-8.5 of 4 °C GWL.The rise in winter T min was mainly concentrated over the central and southern parts of the AP (4 °C GWL ) and extended toward the northern parts of the AP SSP5-8.5 (Fig. 5m-x).The summer T max over AP for SSP1-2.6,SS2-4.5, and SSP5-8.5 shows negligible differences at a 1.5 °C GWL (Table 3).The T max is projected to reach 41.0 °C and 42.5 °C at 3 °C and 4 °C GWLs under SSP5-8.5,respectively.The projected winter T min is 14.7 °C at a 2 °C GWL under SSP2-4.5 and SSP5-8.5, and 17.8 °C at a 4 °C GWL under SSP5-8.5 (Table 3).
The intriguing shifts in the spatial distribution of annual TX90p and TN90p, as depicted in Fig. 6, parallel each other in their responses to global warming.The number of TX90p and TN90P increase more rapidly over the southern parts of AP than in the northern parts, with an increment of more than 80% in the SSP5-8.5 scenario under 4 °C warming.The southern parts of AP have an increase of up to 50 to 60% days of TN90p under this 3 °C warming (Fig. 6).Consistent with increasing temperature (Figs. 2, 3), TX90p and TN90p increase, whereas TX10p and TN10p decrease over AP (Fig. 7).The number of TX10p decreased in the 1.5 °C warming category of SSP5-8.5 from 3 to 6% over AP.The number of TN10p decreased from − 3 days to − 9% days in the 1.5 °C to 3 °C warming category of SSP5-8.5 across the entire AP with a 99% significance level.The changes in TX10p and TN10p decreased by more than − 9 and − 8% days, respectively, over the entire AP under the 4 °C warming category of SSP5-8.5.The TX90p and TN90p are projected to reach up to 80.8% and 86.3%, respectively, at a 4 °C GWL under SSP5-8.5 (Table 3).The TX10p and TN10p are expected to decrease by a maximum of 0.7% and 0.2%, respectively, at a 4 °C GWL under SSP5-8.5 compared to other GWLs (Table 3).
The annual HDs maximum (more than 100 days) changes in the southern part of Saudi Arabia under the 4 °C warming of the SSP5-8.5 (Fig. 8).TR and TN90p show a similar pattern, TR is more than 70 days (> 90 days) over the southern part of AP at 3 °C (4 °C) GWLs under SSP5-8.5 (Fig. 8m-x).The number of HDs and TR in AP is projected to increase by 129 days and 252 days, respectively, at a 3 °C GWL under the SSP5-8.5 scenario (Table 3).At a 4 °C GWL under the same scenario, the HDs and TR are expected to rise by 151 and 286 days, respectively (Table 3).Under various GWLs, the TXx and TNn gradually increase compared to the baseline period (Fig. 9).More significant magnitude variations exist in TXx and TNn throughout the AP.The spatial variation of TXx (TNn) changes indicates an increase in the north and central AP (southern) region compared to the southern (northern) parts of AP.However, the TXx has shown the highest warming of more than 4 °C under 3 °C over the northern parts of AP in the SSP5-8.5.Under the SSP2-4.5 and SSP5-8.5 scenarios at a 2 °C GWL, the TXx (TNn) is projected to increase up to 43.4 °C (~ 8.8 °C) (Table 3).At a 4 °C GWL, the TXx (TNn) is expected to rise to 46.7 °C (11.7 °C) under the SSP5-8.5 scenario (Table 3).
As the warming threshold increases, all temperature indicators in AP are expected to increase, with a notable shift anticipated by the end of the twenty-first century.At high temperatures, the features are more noticeable.The changes in 2 °C warming, all the extremes show significance in SSP2-4.5 and SSP5-8.5 scenarios; note that SSP1-2.6 is not crossing the 2 °C warming level.The overall analysis suggests that the spatial variation of temperature extremes considered in the present study is statistically significant in most of the parts of AP, with remarkable changes in a few indices over the southern regions.In contrast, the changes in the rest of the indices are substantial in the northern parts of the AP.

Discussion
AP is a climate change-sensitive region and is continuously undergoing significant warming.Recent research studies indicated a rise in surface temperature over different parts of AP during the historical and future periods.For instance, Almazouri et al. 46 analyzed the Climate Research Unit (CRU) data and revealed that Saudi Arabia experienced a temperature rise of 0.72 °C and 0.51 °C per decade in the dry and wet seasons from 1979-2009 respectively.The ground-based observations and reanalysis of temperatures also showed an increasing trend in mean annual temperature over the AP 7,34 .
Given this significant warming in the AP region, this study was designed to estimate future temperature changes and their extremes over the AP for four different GWLs and three SSP scenarios.The results revealed that the mean annual cycle of the monthly temperatures of MMM, along with the ERA5, provide better insights into the comparison of modeled data with the reanalysis data set over the study region, where the long-term insitu observations are not fully compatible in comparison to gridded modeled data.ERA5 is a proven dataset over this region, showing that the long-term climate variability is appropriate, as Bawadekji et al. 47 reported.Fig. S1 shows that the NEX-GDDP-CMIP6 MMM was slightly underestimated during the historical period compared to ERA5 data.Similar variability of the models' simulation has been observed with the bias-corrected models over India when analyzed with the MMM of 13 CMIP6 models 48 .The model simulation could underestimate the mean monthly temperatures obtained from the India Meteorological Department dataset.
The model evaluation of the present study is shown in Fig. 1, unraveling a lesser bias with the MMM of the NEX-GDDP-CMIP6.Almazroui et al. 10 used the CMIP6 data from the 31 models to study the future changes in climate over the AP region.CMIP6 data inferred a higher climate variability over the study region when compared with the CMIP5 data.In this perspective, the present study is of more relevance because it uses biascorrected and high-resolution downscaled data from the NEX-GDDP-CMIP6 project.In addition, using MMM helps reduce the uncertainties originating from the raw CMIP6 data 23 .As reported by Ajjur and Al-Ghamdi 28 , AP is one of the global change hotspots, and the results obtained from the temporal anomalies of summer T max and winter T min over the AP region in the present study witness the same as the warming is going beyond 5 °C under SSP5-8.5 scenario by the end of the twenty-first century.These trends have been supported by the different regional trends over AP, reported by several studies 10,13,14 .Using extreme temperature indices under different GWLs in the AP region makes the difference between the present work and the existing literature.The projected effects of climate variables under various warnings, such as 1.5 °C, 2 °C, 3 °C, and 4 °C have been widely reported across different regions of the globe, including Africa 49 , China 50 , the Caribbean region 51 , and South Asia 52 .As mentioned earlier, there are few studies on projected changes in temperatures and their extremes over the AP region under different SSPs; these changes have not been discussed in terms of varying warming levels, such as 1.5 °C, 2 °C, 3 °C, and 4 °C.To address this limitation, the present study quantifies the future variability of temperatures and their extremes in the AP region under four GWLs and three SSP scenarios.The study findings revealed more warming in summer T max (winter T min ) and related extremes over AP's northern (southern) parts.Similar results were reported by Almazrouri et al. 10 in the near and far future periods.For instance, they projected that the northern parts of AP during the winter season will likely experience an increase of 4.1-5.8°C, while the southern regions are expected to have lesser warming.Likewise, in the present study, we have found major warming over northern parts of AP but with higher values than reported by Almazrouri et al. 10 .Our analysis revealed more warming witnessed, which can be attributed to the use of bias-corrected and high-resolution model simulations.Further, the increment of most of the temperature extremes poses threats that need to be tackled, particularly in the 4 °C warming of the SSP5-8.5 scenario.The southeastern parts of the AP, particularly southern South Arabia, Yemen, Oman, Qatar, and the United Arab Emirates, are to be the major focus in the warming scenario context, as these regions showed prominent changes in temperature extremes in all the warming categories of SSP5-8.5.

Conclusions
Determining the future locations and levels of risk to lives and livelihoods requires understanding projected climate change and its spatial heterogeneity.Policymakers must fulfill this prerequisite to develop more realistic strategies for future climate change adaptation and mitigation.In the present study, we employed the highresolution, statistically downscaled, and bias-corrected NEX-GDDP-CMIP6 models to estimate future temperature changes and their extremes over the AP region for four GWLs and three SSPs.The results suggest the NEX-GDDP-CMIP6 models can reproduce the monthly annual cycle pattern compared with ERA5 data during the reference period.The NEX-GDDP-CMIP6 individual and MMM showed the reproducing characteristics of extreme temperature events over the AP region.The summer T max (winter T min ) is expected to increase by 0.11-0.67°C (0.09-0.69 °C) per decade under the selected SSPs by the end of the twenty-first century.The highest increase in summer T max (winter T min ) is expected to be observed in the central and northern parts of AP (southern part of AP) by the end of the twenty-first century.The results further reveal spatial annual variations in the patterns and magnitudes of temperature extremes, with substantial differences between the selected GWLs and SSPs.For 1.5 °C-4 °C GWLs, the annual changes in high-temperature extremes such as TX90p, TN90p, HDs, TR, TXx, and TNn are significantly increasing over the southern and central-north of AP, while the TX10p

Figure 1 .
Figure 1.Performance of the NEX-GDDP-CMIP6 models in simulating historical temperatures and their extreme indices over the Arabian Peninsula (AP) region during the period 1995-2014; (a) Root mean square error (RMSE) and (b) standard deviation (SD), relative to the ERA5.The values are spatially averaged over AP for different climate indices of individual NEX-GDDP-CMIP6 models and multi-model mean (MMM).

Figure 5 .
Figure 5. Spatial distribution of projected changes in summer T max and winter T min over the AP region under the selected GWLs and SSPs relative to the reference period (1995-2014); (a-l) summer T min and (m-x) winter T min .Dotted regions represent significant changes at a 99% significance level.

Figure 6 .
Figure 6.Spatial distribution of projected changes in annual warm days (TX90p) and warm nights (TN90p) over the AP region under the selected GWLs and SSPs relative to the reference period (1995-2014); (a-l) warm days (TX90p) and (m-x) warm nights (TN90p).Dotted regions represent significant changes at a 99% significance level.

Figure 7 .
Figure 7. Spatial distribution of projected changes in annual cold days (TX10p) and cold nights (TN10p) over the AP region under the selected GWLs and SSPs, relative to the reference period (1995-2014); (a-l) cold days (TX10p) and (m-x) cold nights (TN10p).Dotted regions represent significant changes at a 99% significance level.

Figure 8 .
Figure 8. Spatial distribution of projected changes in annual hot days (HDs) and tropical nights (TR) over the AP region under the selected GWLs and SSPs, relative to the reference period (1995-2014); (a-l) hot days (HDs) and (m-x) tropical nights (TR).Dotted regions represent significant changes at a 99% significance level.

Figure 9 .
Figure 9. Spatial distribution of projected changes in annual maximum of T max (TXx) and minimum of T min (TNn) over the AP region under the selected GWLs and SSPs, relative to the reference period (1995-2014); (a-l) maximum of T max (TXx) and (m-x) minimum of T min (TNn).Dotted regions represent significant changes at a 99% significance level.

Table 2 .
Description of extreme temperature indices used in this study.

Table 3 .
Area-averaged values of temperatures and their extreme temperature indices under different GWLs and SSPs over the AP region.